Bayesian Spatial Modeling Of Housing Prices Subject To A Localized Externality
Geostatistics, Hedonic regression, Monte Carlo, Random field, Real estate data
Communications in Statistics - Theory and Methods
This work proposes a non stationary random field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is fit using a Bayesian approach via a Markov chain Monte Carlo algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model.
Department of Mathematics
Original Publication Date
DOI of published version
Ecker, Mark D. and De Oliveira, Victor, "Bayesian Spatial Modeling Of Housing Prices Subject To A Localized Externality" (2008). Faculty Publications. 2505.